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Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data

  • Wei Chen
  • Xiaobo ZhouEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1939)

Abstract

The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. L1000 big datasets provide gene expression profiles induced by over 10,000 compounds, shRNAs, and kinase inhibitors using L1000 platform. We developed a systematic compound signature discovery pipeline named csNMF, which covers from raw L1000 data processing to drug screening and mechanism generation. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. In this way, the potential mechanisms of compounds’ efficacy are elucidated by our computational model.

Key words

LINCS L1000 csNMF Compound signature Drug signature Compound efficacy 

Notes

Acknowledgment

The work was supported by the grants of NIH U01HL111560-04 (Zhou) and NIH U01CA166886-03 (Zhou).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of RadiologyWake Forest University Medical SchoolWinston-SalemUSA
  2. 2.School of Biomedical InformaticsThe University of Texas, Health Science Center at HoustonHoustonUSA

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